Application of Fourier-transform ion cyclotron resonance mass spectrometry to metabolic profiling and metabolite identification

Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Applications for Metabolomics

Metabolomics is an interdisciplinary field that aims to study all metabolites < 1500 Da that are ubiquitously found within all organisms. Metabolomics is experiencing exponential growth and commonly relies on high-resolution mass spectrometry (HRMS). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a form of HRMS that is particularly well suited for metabolomics research due to its exceptionally high resolution (105-106) and sensitivity with a mass accuracy in parts per billion (ppb). In this regard, FT-ICR-MS can provide valuable insights into the metabolomics analysis of complex biological systems due to unique capabilities such as the easy separation of isobaric and isomeric species, isotopic fine structure analysis, spatial resolution of metabolites in cells and tissues, and a high confidence (<1 ppm mass error) in metabolite identification. Alternatively, the large and complex data sets, long acquisition times, high cost, and limited access mainly through national mass spectrometry facilities may impede the routine adoption of FT-ICR-MS by metabolomics researchers. This review examines recent applications of FT-ICR-MS metabolomics in the search for clinical and non-human biomarkers; for the analysis of food, beverage, and environmental samples; and for the high-resolution imaging of tissues and other biological samples. We provide recent examples of metabolomics studies that highlight the advantages of FT-ICR-MS for the detailed and reliable characterization of the metabolome. Additionally, we offer some practical considerations for implementing FT-ICR-MS into a research program by providing a list of FT-ICR-MS facilities and by identifying different high-throughput interfaces, varieties of sample types, analysis methods (e.g., van Krevelen diagrams, Kendrick mass defect plot, etc.), and sample preparation and handling protocols used in FT-ICR-MS experiments. Overall, FT-ICR-MS holds great promise as a vital research tool for advancing metabolomics investigations.

Implications of the Cultivation of Rosemary and Thyme (Lamiaceae) in Plant Communities for the Development of Antioxidant Therapies

Oxidative stress is the most critical factor in multiple functional disorders' development, and natural antioxidants could protect the human body against it. Our study aims to investigate the polyphenol content of four extracts of two medicinal plants (Rosmarinus officinalis L. and Thymus vulgaris L.) and analyze the correlation with their antioxidant activity. The research was carried out on extracts of rosemary and thyme obtained from species cultivated together in plant communities. Both were compared with extracts from species cultivated in individual crops (control crops). Their polyphenols were determined by spectrophotometric methods (dosage of flavones, phenol carboxylic acids, and total polyphenols) and chromatography (UHPLC-MS and FT-ICR MS). Triterpenic acids were also quantified, having a higher concentration in the thyme extract from the culture. The antioxidant activity of the dry extracts was evaluated in vitro (DPPH, ABTS, and FRAP) and in silico (prediction of interactions with BACH1/BACH2 transcription factors). The concentrations of polyphenols are higher in the extracts obtained from the sources collected from the common crops. These observations were also validated following the chromatographic analysis for some compounds. Statistically significant differences in the increase in the antioxidant effect were observed for the extracts from the common batches compared to those from the individual ones. Following the Pearson analysis, the IC₅₀ values for each plant extract were strongly correlated with the concentration of active phytoconstituents. Molecular docking studies revealed that quercetin could bind to BTB domains of BACH1 and BACH2 transcription factors, likely translating into increased antioxidant enzyme expression. Future studies must validate the in silico findings and further investigate phytosociological cultivation's effects.

Hyphenation of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with separation methods: The art of compromises and the possible - A review

This review provides an overview of the online hyphenation of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) with separation methods to date. The online coupling between separation techniques (gas and liquid chromatography, capillary electrophoresis) and FT-ICR MS essentially raises questions of compromise and is not look as straightforward as hyphenation with other analyzers (QTOF-MS for instance). FT-ICR MS requires time to reach its highest resolving power and accuracy in mass measurement capabilities whereas chromatographic and electrophoretic peaks are transient. In many applications, the strengths and the weaknesses of each technique are balanced by their hyphenation. Untargeted "Omics" (e.g. proteomics, metabolomics, petroleomics, …) is one of the main areas of application for FT-ICR MS hyphenated to online separation techniques because of the complexity of the sample. FT-ICR MS achieves the required high mass measurement accuracy to determine accurate molecular formulae and resolution for isobar distinction. Meanwhile separation techniques highlight isomers and reduce the ion suppression effects extending the dynamic range. Even if the implementation of FT-ICR MS hyphenated with online separation methods is a little trickier (the art of compromise), this review shows that it provides unparalleled results to the scientific community (the art of the possible), along with raising the issue of its future in the field with the relentless technological progress.

Comparison of High-Resolution Fourier Transform Mass Spectrometry Platforms for Putative Metabolite Annotation

Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap mass spectrometry (MS) are among the highest-performing analytical platforms used in metabolomics. Non-targeted metabolomics experiments, however, yield extremely complex datasets that make metabolite annotation very challenging and sometimes impossible. The high-resolution accurate mass measurements of the leading MS platforms greatly facilitate this process by reducing mass errors and spectral overlaps. When high resolution is combined with relative isotopic abundance (RIA) measurements, heuristic rules, and constraints during searches, the number of candidate elemental formula(s) can be significantly reduced. Here, we evaluate the performance of Orbitrap ID-X and 12T solariX FT-ICR mass spectrometers in terms of mass accuracy and RIA measurements and how these factors affect the assignment of the correct elemental formulas in the metabolite annotation pipeline. Quality of the mass measurements was evaluated under various experimental conditions (resolution: 120, 240, 500 K; automatic gain control: 5 × 104, 1 × 105, 5 × 105) for the Orbitrap MS platform. High average mass accuracy (<1 ppm for UPLC-Orbitrap MS and <0.2 ppm for direct infusion FT-ICR MS) was achieved and allowed the assignment of correct elemental formulas for over 90% (m/z 75-466) of the 104 investigated metabolites. 13C1 and 18O1 RIA measurements further improved annotation certainty by reducing the number of candidates. Overall, our study provides a systematic evaluation for two leading Fourier transform (FT)-based MS platforms utilized in metabolite annotation and provides the basis for applying these, individually or in combination, to metabolomics studies of biological systems.

Fate of Labile Organic Carbon in Paddy Soil Is Regulated by Microbial Ferric Iron Reduction

Global paddy soil is the primary source of methane, a potent greenhouse gas. It is therefore highly important to understand the carbon cycling in paddy soil. Microbial reduction of iron, which is widely found in paddy soil, is likely coupled with the oxidation of dissolved organic matter (DOM) and suppresses methanogenesis. However, little is known about the biotransformation of small molecular DOM accumulated under flooded conditions and the effect of iron reduction on the biotransformation pathway. Here, we carried out anaerobic incubation experiments using field-collected samples amended with ferrihydrite and different short-chain fatty acids. Our results showed that less than 20% of short-chain fatty acids were mineralized and released to the atmosphere. Using Fourier transform ion cyclotron resonance mass spectrometry, we further found that a large number of recalcitrant molecules were produced during microbial consumption of these short-chain fatty acids. Moreover, the biotransformation efficiency of short-chain fatty acids decreased with the increasing length of carbon chains. Ferrihydrite addition promoted microbial assimilation of short-chain fatty acids as well as enhanced the activation and biotransformation of indigenous stable carbon in the soil replenished with formate. This study demonstrates the significance of ferrihydrite in the biotransformation of labile DOM and promotes a more comprehensive understanding of the coupling of iron reduction and carbon cycling in paddy soils.

Metabolomics Profiling before, during, and after the Beijing Olympics: A Panel Study of Within-Individual Differences during Periods of High and Low Air Pollution

BACKGROUND

The metabolome is a collection of exogenous chemicals and metabolites from cellular processes that may reflect the body's response to environmental exposures. Studies of air pollution and metabolomics are limited.

OBJECTIVES

To explore changes in the human metabolome before, during, and after the 2008 Beijing Olympics Games, when air pollution was high, low, and high, respectively.

METHODS

Serum samples were collected before, during, and after the Olympics from 26 participants in an existing panel study. Gas and ultra-high performance liquid chromatography/mass spectrometry were used in metabolomics analysis. Repeated measures ANOVA, network analysis, and enrichment analysis methods were employed to identify metabolites and classes associated with air pollution changes.

RESULTS

A total of 886 molecules were measured in our metabolomics analysis. Network partitioning identified four modules with 65 known metabolites that significantly changed across the three time points. All known molecules in the first module ([Formula: see text]) were lipids (e.g., eicosapentaenoic acid, stearic acid). The second module consisted primarily of dipeptides ([Formula: see text], e.g., isoleucylglycine) plus 8 metabolites from four other classes (e.g., hypoxanthine, 12-hydroxyeicosatetraenoic acid). Most of the metabolites in Modules 3 (19 of 23) and 4 (5 of 5) were unknown. Enrichment analysis of module-identified metabolites indicted significantly overrepresented pathways, including long- and medium-chain fatty acids, polyunsaturated fatty acids (n3 and n6), eicosanoids, lysolipid, dipeptides, fatty acid metabolism, and purine metabolism [(hypo) xanthine/inosine-containing pathways].

CONCLUSIONS

We identified two major metabolic signatures: one consisting of lipids, and a second that included dipeptides, polyunsaturated fatty acids, taurine, and xanthine. Metabolites in both groups decreased during the 2008 Beijing Olympics, when air pollution was low, and increased after the Olympics, when air pollution returned to normal (high) levels. https://doi.org/10.1289/EHP3705.

Tandem mass spectrometry methods to accelerate the identification of phytotoxic metabolites produced by Streptomyces sp. 39 PL

Natural products isolated by microorganisms are interesting in the search for new compounds with several biological activities. However, low concentration and structural diversity make the isolation a time-consuming step. Tandem mass spectrometry is a well-established technology for the identification and characterization of target microbial natural products due to high sensitivity and selectivity of these experiments. We developed a method employing neutral loss experiments (LC-ESI-MS/MS) to identify luminacins in microbial crude extracts. The luminacins class exhibited conserved fragmentation pattern with loss at 172 Da relative to glycosides fragment and this loss was used in searching for compounds belonging to this class. Therefore, the crude extract produced by Streptomyces sp. 39 PL was analysed and five luminacins were isolated - one is a novel luminacin I at 466 Da.

Van Krevelen diagram visualization of high resolution-mass spectrometry metabolomics data with OpenVanKrevelen

INTRODUCTION

Van Krevelen (VK) diagrams provide a promising but uncommon solution to a number of challenges associated with the visualization of metabolomics data. VK diagrams are created by plotting H:C ratios against O:C ratios of the compounds in a chemical mixture.

OBJECTIVES

The aim of this manuscript is to present an open-source software tool and reference map that we have developed to make VK diagrams for visualization of metabolomics data.

METHODS

Software was created with a prompt-driven command line user interface and was written using Python 2.7. We empirically derived an accompanying map by plotting where compounds from seven biomolecule types fall within the VK plot space.

RESULTS

We've created an easy to use, open source software tool named OpenVanKrevelen for making a range of VK diagrams that is available on GitHub: https://github.com/HegemanLab/VanKrevelenLocal . The empirical mapping approach has produced several improvements from previously published maps.

CONCLUSIONS

OpenVanKrevelen provides the metabolomics community with access to a new tool for visualization of complex metabolomics datasets.

Pathway-specific metabolome analysis with 18O2-labeled Medicago truncatula via a mass spectrometry-based approach

INTRODUCTION

Oxygen from carbon dioxide, water or molecular oxygen, depending on the responsible enzyme, can lead to a large variety of metabolites through chemical modification.

OBJECTIVES

Pathway-specific labeling using isotopic molecular oxygen (¹⁸O₂) makes it possible to determine the origin of oxygen atoms in metabolites and the presence of biosynthetic enzymes (e.g., oxygenases). In this study, we established the basis of ¹⁸O₂-metabolome analysis.

METHODS

¹⁸O₂ labeled whole Medicago truncatula seedlings were prepared using ¹⁸O₂-air and an economical sealed-glass bottle system. Metabolites were analyzed using high-accuracy and high-resolution mass spectrometry. Identification of the metabolite was confirmed by NMR following UHPLC-solid-phase extraction (SPE).

RESULTS

A total of 511 peaks labeled by ¹⁸O₂ from shoot and 343 peaks from root were annotated by untargeted metabolome analysis. Additionally, we identified a new flavonoid, apigenin 4'-O-[2'-O-coumaroyl-glucuronopyranosyl-(1-2)-O-glucuronopyranoside], that was labeled by ¹⁸O₂. To the best of our knowledge, this is the first report of apigenin 4'-glucuronide in M. truncatula. Using MSⁿ analysis, we estimated that ¹⁸O atoms were specifically incorporated in apigenin, the coumaroyl group, and glucuronic acid. For apigenin, an ¹⁸O atom was incorporated in the 4'-hydroxy group. Thus, non-specific incorporation of an ¹⁸O atom by recycling during one month of labeling is unlikely compared with the more specific oxygenase-catalyzing reaction.

CONCLUSION

Our finding indicated that ¹⁸O₂ labeling was effective not only for the mining of unknown metabolites which were biosynthesized by oxygenase-related pathway but also for the identification of metabolites whose oxygen atoms were derived from oxygenase activity.

Seed Metabolome Analysis of a Transgenic Rice Line Expressing Cholera Toxin B-subunit

Plant-based human vaccines have been actively developed in recent years, and rice (Oryza sativa L.) is one of the best candidate crops for their production and delivery. By expressing a modified cholera toxin B (CTB) subunit, we previously developed MucoRice-CTB, a rice-based vaccine against cholera, which is caused by infection of the intestine with the bacteria Vibrio cholerae. MucoRice-CTB lines have been extensively characterized by whole-genome sequencing and proteome analyses to evaluate the mutation profiles and proteome status, respectively. Here, we report non-targeted metabolomic profiling of the MucoRice-CTB transgenic rice line 51A (MR-CTB51A), MucoRice-RNAi (MR-RNAi), and their non-transgenic parent line by using gas chromatography-time-of-flight mass spectrometry. The levels of several amino acids, organic acids, carbohydrates, lipids, and secondary metabolites were significantly increased in MR-CTB51A compared with the non-transgenic parent line. These metabolomics results complement essential information obtained by genome sequencing and proteomics approaches, thereby contributing to comprehensive understanding of the properties of MucoRice-CTB as a plant-based vaccine.

Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress

Metabolomics is one omics approach that can be used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include analysis of a wide range of chemical species with diverse physical properties, from ionic inorganic compounds to biochemically derived hydrophilic carbohydrates, organic and amino acids, and a range of hydrophobic lipid-related compounds. This complexitiy brings huge challenges to the analytical technologies employed in current plant metabolomics programs, and powerful analytical tools are required for the separation and characterization of this extremely high compound diversity present in biological sample matrices. The use of mass spectrometry (MS)-based analytical platforms to profile stress-responsive metabolites that allow some plants to adapt to adverse environmental conditions is fundamental in current plant biotechnology research programs for the understanding and development of stress-tolerant plants. In this review, we describe recent applications of metabolomics and emphasize its increasing application to study plant responses to environmental (stress-) factors, including drought, salt, low oxygen caused by waterlogging or flooding of the soil, temperature, light and oxidative stress (or a combination of them). Advances in understanding the global changes occurring in plant metabolism under specific abiotic stress conditions are fundamental to enhance plant fitness and increase stress tolerance. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:620-649, 2016.

In situ characterizing membrane lipid phenotype of breast cancer cells using mass spectrometry profiling

Lipid composition in cell membrane is closely associated with cell characteristics. Here, matrix-assisted laser desorption/ionization- Fourier transform ion cyclotron resonance mass spectrometry was employed to in situ determine membrane components of human mammary epithelial cells (MCF-10 A) and six different breast cancer cell lines (i.e., BT-20, MCF-7, SK-BR-3, MDA-MB-231, MDA-MB-157, and MDA-MB-361) without any lipid extraction and separation. Partial least-square discriminant analysis indicated that changes in the levels of these membrane lipids were closely correlated with the types of breast cell lines. Elevated levels of polyunsaturated lipids in MCF-10 A cells relative to six breast cancer cells and in BT-20 cells relative to other breast cancer cell lines were detected. The Western blotting assays indicated that the expression of five lipogenesis-related enzymes (i.e., fatty acid synthase 1(FASN1), stearoyl-CoA desaturase 1 (SCD1), stearoyl-CoA desaturase 5 (SCD5), choline kinase α (CKα), and sphingomyelin synthase 1) was associated with the types of the breast cells, and that the SCD1 level in MCF-7 cells was significantly increased relative to other breast cell lines. Our findings suggest that elevated expression levels of FASN1, SCD1, SCD5, and CKα may closely correlated with enhanced levels of saturated and monounsaturated lipids in breast cancer cell lines.

The re-emergence of natural products for drug discovery in the genomics era

Natural products have been a rich source of compounds for drug discovery. However, their use has diminished in the past two decades, in part because of technical barriers to screening natural products in high-throughput assays against molecular targets. Here, we review strategies for natural product screening that harness the recent technical advances that have reduced these barriers. We also assess the use of genomic and metabolomic approaches to augment traditional methods of studying natural products, and highlight recent examples of natural products in antimicrobial drug discovery and as inhibitors of protein-protein interactions. The growing appreciation of functional assays and phenotypic screens may further contribute to a revival of interest in natural products for drug discovery.

High resolution mass spectrometry based techniques at the crossroads of metabolic pathways

The metabolome is the set of small molecular mass compounds found in biological media, and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. It is a data driven and multidisciplinary approach combining analytical chemistry for data acquisition, and biostatistics, informatics and biochemistry for mining and interpretation of these data. Since the middle of the 2000s, high resolution mass spectrometry is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. As the field of HRMS is quickly and permanently evolving, the aim of this work is to review its use in different aspects of metabolomics, including data acquisition, metabolite annotation, identification and quantification. At last, we would like to show that, thanks to their versatility, HRMS instruments are the most appropriate to achieve optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics.

High-throughput discovery metabolomics

Non-targeted metabolomics by mass spectrometry has established as the method of choice for investigating metabolic phenotypes in basic and applied research. Compared to other omics, metabolomics provides broad scope and yet direct information on the integrated cellular response with low demand in material and sample preparation. These features render non-targeted metabolomics ideally suited for large scale screens and discovery. Here we review the achievements and potential in high-throughput, non-targeted metabolomics. We found that routine and precise analysis of thousands of small molecular features in thousands of complex samples per day and instrument is already reality, and ongoing developments in microfluidics and integrated interfaces will likely further boost throughput in the next few years.

Exploring the Arabidopsis sulfur metabolome

Sulfur plays a crucial role in protein structure and function, redox status and plant biotic stress responses. However, our understanding of sulfur metabolism is limited to identified pathways. In this study, we used a high-resolution Fourier transform mass spectrometric approach in combination with stable isotope labeling to describe the sulfur metabolome of Arabidopsis thaliana. Databases contain roughly 300 sulfur compounds assigned to Arabidopsis. In comparative analyses, we showed that the overlap of the expected sulfur metabolome and the mass spectrometric data was surprisingly low, and we were able to assign only 37 of the 300 predicted compounds. By contrast, we identified approximately 140 sulfur metabolites that have not been assigned to the databases to date. We used our method to characterize the γ-glutamyl transferase mutant ggt4-1, which is involved in the vacuolar breakdown of glutathione conjugates in detoxification reactions. Although xenobiotic substrates are well known, only a few endogenous substrates have been described. Among the specifically altered sulfur-containing masses in the ggt4-1 mutant, we characterized one endogenous glutathione conjugate and a number of further candidates for endogenous substrates. The small percentage of predicted compounds and the high proportion of unassigned sulfur compounds identified in this study emphasize the need to re-evaluate our understanding of the sulfur metabolome.

Ultrahigh resolution mass spectrometry-based metabolic characterization reveals cerebellum as a disturbed region in two animal models

In the previous reports about cognitive dysfunction, cerebellum was thought to be a less affected tissue by genetic or environmental alterations in comparison to other tissues in the brain including hippocampus under the same conditions. In this work, we investigated two types of metabolomic alterations inside the cerebellum tissue. The first one addressed the differences in the metabolomics profiles between Transgenic (Tg) CRND8 of Alzheimer's disease mice and non-transgenic (non-Tg) littermates. The second one addressed the metabolic differences between wild type mice exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and wild type mice which are not exposed to this toxic compound. For these two investigations, ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS) was implemented. As a result, the significant changes of each comparison were tentatively annotated by the high mass accuracy generated from the measurements in the negative ion mode. The biosynthesis of amino acids was also enhanced pronouncedly, and perturbation of purine metabolism was also observed in Tg mice compared to non-Tg littermates. In another animal model, the reduced levels of amino acids were found whereas the intermediate levels in purine metabolism and fatty acids including fatty acid conjugated metabolites were elevated in cerebellar tissues of mice exposed to TCDD compared to control group. Collectively, it was demonstrated that FT-ICR/MS was a powerful tool for interpretation of the elemental compositions of the peaks, revealing that the metabolic perturbations in cerebellar tissues of mice were induced by either genetic manipulation or environmental factor. Therefore, the non-targeted approach, alternatively, provides various metabolic phenotypes for the systems-level mirror of the complex etiology of neurotoxicity in the cerebellum.

Metabolic features of Protochlamydia amoebophila elementary bodies--a link between activity and infectivity in Chlamydiae

The Chlamydiae are a highly successful group of obligate intracellular bacteria, whose members are remarkably diverse, ranging from major pathogens of humans and animals to symbionts of ubiquitous protozoa. While their infective developmental stage, the elementary body (EB), has long been accepted to be completely metabolically inert, it has recently been shown to sustain some activities, including uptake of amino acids and protein biosynthesis. In the current study, we performed an in-depth characterization of the metabolic capabilities of EBs of the amoeba symbiont Protochlamydia amoebophila. A combined metabolomics approach, including fluorescence microscopy-based assays, isotope-ratio mass spectrometry (IRMS), ion cyclotron resonance Fourier transform mass spectrometry (ICR/FT-MS), and ultra-performance liquid chromatography mass spectrometry (UPLC-MS) was conducted, with a particular focus on the central carbon metabolism. In addition, the effect of nutrient deprivation on chlamydial infectivity was analyzed. Our investigations revealed that host-free P. amoebophila EBs maintain respiratory activity and metabolize D-glucose, including substrate uptake as well as host-free synthesis of labeled metabolites and release of labeled CO₂ from ¹³C-labeled D-glucose. The pentose phosphate pathway was identified as major route of D-glucose catabolism and host-independent activity of the tricarboxylic acid (TCA) cycle was observed. Our data strongly suggest anabolic reactions in P. amoebophila EBs and demonstrate that under the applied conditions D-glucose availability is essential to sustain metabolic activity. Replacement of this substrate by L-glucose, a non-metabolizable sugar, led to a rapid decline in the number of infectious particles. Likewise, infectivity of Chlamydia trachomatis, a major human pathogen, also declined more rapidly in the absence of nutrients. Collectively, these findings demonstrate that D-glucose is utilized by P. amoebophila EBs and provide evidence that metabolic activity in the extracellular stage of chlamydiae is of major biological relevance as it is a critical factor affecting maintenance of infectivity.

The Chlamydiae are a group of bacteria that strictly rely on eukaryotic host cells as a niche for intracellular growth. This group includes major pathogens of humans and animals as well as symbionts of protists. Unlike most other bacteria, chlamydiae alternate between two distinct developmental stages. Here we provide novel insights into the infective stage, the elementary body (EB), which has been described almost a century ago and is commonly referred to as an inert spore-like particle. Our analyses of EBs of the amoeba symbiont Protochlamydia amoebophila provide a detailed overview of their metabolism outside of, and independent from, their natural host cells. We demonstrated that these EBs are capable of respiration and are active in the major routes of central carbon metabolism, including glucose import, biosynthetic reactions, and catabolism for energy generation. Glucose starvation resulted in a rapid decline of metabolic activity in P. amoebophila EBs and a concomitant decrease in their potential to infect new host cells. The human pathogen Chlamydia trachomatis was also dependent on nutrient availability for extracellular survival. The extent of metabolic activity in chlamydial EBs and its consequences for infectivity challenge long-standing textbook knowledge and demonstrate that the infective stage is far more dependent on its environment than previously recognized.

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology

Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data. The present study reviews the usage of KNApSAcK Family DB in metabolomics and related area, discusses several statistical methods for handling multivariate data and shows their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot reveals many plants are rarely utilized while some plants are highly utilized toward specific efficacy. Furthermore, the ingredients of Jamu formulas are modeled using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. This model produces 71.6% correct classification in predicting efficacy. Permutation test then is used to determine plants that serve as main ingredients in Jamu formula by evaluating the significance of the PLS-DA coefficients. Next, in order to explain the role of plants that serve as main ingredients in Jamu medicines, information of pharmacological activity of the plants is added to the predictor block. Then N-PLS-DA model, multiway version of PLS-DA, is utilized to handle the three-dimensional array of the predictor block. The resulting N-PLS-DA model reveals that the effects of some pharmacological activities are specific for certain efficacy and the other activities are diverse toward many efficacies. Mathematical modeling introduced in the present study can be utilized in global analysis of big data targeting to reveal the underlying biology.

Extraction, derivatization, and determination of metabolome in human macrophages

A GC/TOF-MS was applied to the determination of metabolites in human macrophages. The extraction conditions and quenching conditions were investigated and optimized. The results indicated that 0.9% w/v sodium chloride at 4°C was the most favorable condition to quench macrophage, 1 mL 50% ACN for 2 min in ice bath was the optimal condition to extract 5 × 10(6) cells. Two hundred six peaks could be detectable with peak area over 50 using this method. Among these peaks, 45 peaks with the similarity over 700 were identified using standard compounds for endogenous metabolites. Thirty-seven out of 45 metabolites could be quantified directly by this method. Twenty metabolites were selected randomly, and 15 amino acids were used for method validation. The correlation coefficients (r) ranging from 0.9902 to 0.9977 were obtained for 15 amino acids in the range of 2.35-150.20 μg/mL. The intraday and interday precisions were lower than 19.90% for the randomly selected 20 endogenous metabolites. Using this development method and multivariate statistical technique, several potential biomarkers were found from human macrophages infected by different Mycobacterium tuberculosis (M. tuberculosis) strains. The results suggest that the method could be applied to the investigation of the pathogenicity of tuberculosis.

Hippocampal metabolomics using ultrahigh-resolution mass spectrometry reveals neuroinflammation from Alzheimer's disease in CRND8 mice

In the wake of genomics, metabolomics characterizes the small molecular metabolites revealing the phenotypes induced by gene mutants. To address the metabolic signatures in the hippocampus of the amyloid-beta (Aβ) peptides produced in transgenic (Tg) CRND8 mice, high-field ion cyclotron resonance-Fourier transform mass spectrometry supported by LC-LTQ-Orbitrap was introduced to profile the extracted metabolites. More than 10,000 ions were detected in the mass profile for each sample. Subsequently, peak alignment and the 80% rule followed by feature selection based on T score computation were performed. The putative identification was also conducted using the highly accurate masses with isotopic distribution by interfacing the MassTRIX database as well as MS/MS fragmentation generated in the LTQ-Orbitrap after chromatographic separation. Consequently, 58 differentiating masses were tentatively identified while up to 44 differentiating elemental compositions could not be biologically annotated in the databases. Nonetheless, of the putatively annotated masses, eicosanoids in arachidonic acid metabolism, fatty acid beta-oxidation disorders as well as disturbed glucose metabolism were highlighted as metabolic traits of Aβ toxicity in Tg CRND8 mice. Furthermore, a web-based bioinformatic tool was used for simulation of the metabolic pathways. As a result of the obtained metabolic signatures, the arachidonic acid metabolism dominates the metabolic perturbation in hippocampal tissues of Tg CRND8 mice compared to non-Tg littermates, indicating that Aβ toxicity functions neuroinflammation in hippocampal tissue and new theranostic opportunities might be offered by characterization of altered arachidonic acid metabolism for Alzheimer's disease.

Characterization of dissolved organic matter in full scale continuous stirred tank biogas reactors using ultrahigh resolution mass spectrometry: a qualitative overview

Dissolved organic matter (DOM) was characterized in eight full scale continuous stirred tank biogas reactors (CSTBR) using solid-phase extraction and electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FT-ICR-MS). An overview of the DOM molecular complexity in the samples from biogas reactors with conventional operational conditions and various substrate profiles is provided by assignments of unambiguous exact molecular formulas for each measured mass peak. Analysis of triplicate samples for each reactor demonstrated the reproducibility of the solid-phase extraction procedure and ESI-FT-ICR-MS which allowed precise evaluation of the DOM molecular differences among the different reactors. Cluster analysis on mass spectrometric data set showed that the biogas reactors treating sewage sludge had distinctly different DOM characteristics compared to the codigesters treating a combination of organic wastes. Furthermore, the samples from thermophilic and mesophilic codigesters had different DOM composition in terms of identified masses and corresponding intensities. Despite the differences, the results demonstrated that compositionally linked organic compounds comprising 28-59% of the total number of assigned formulas for the samples were shared in all the reactors. This suggested that the shared assigned formulas in studied CSTBRs might be related to common biochemical transformation in anaerobic digestion process and therefore, performance of the CSTBRs.

A yeast metabolite extraction protocol optimised for time-series analyses

There is an increasing call for the absolute quantification of time-resolved metabolite data. However, a number of technical issues exist, such as metabolites being modified/degraded either chemically or enzymatically during the extraction process. Additionally, capillary electrophoresis mass spectrometry (CE-MS) is incompatible with high salt concentrations often used in extraction protocols. In microbial systems, metabolite yield is influenced by the extraction protocol used and the cell disruption rate. Here we present a method that rapidly quenches metabolism using dry-ice ethanol bath and methanol N-ethylmaleimide solution (thus stabilising thiols), disrupts cells efficiently using bead-beating and avoids artefacts created by live-cell pelleting. Rapid sample processing minimised metabolite leaching. Cell weight, number and size distribution was used to calculate metabolites to an attomol/cell level. We apply this method to samples obtained from the respiratory oscillation that occurs when yeast are grown continuously.

The Arabidopsis glucosyltransferase UGT76B1 conjugates isoleucic acid and modulates plant defense and senescence

Plants coordinate and tightly regulate pathogen defense by the mostly antagonistic salicylate (SA)- and jasmonate (JA)-mediated signaling pathways. Here, we show that the previously uncharacterized glucosyltransferase UGT76B1 is a novel player in this SA-JA signaling crosstalk. UGT76B1 was selected as the top stress-induced isoform among all 122 members of the Arabidopsis thaliana UGT family. Loss of UGT76B1 function leads to enhanced resistance to the biotrophic pathogen Pseudomonas syringae and accelerated senescence but increased susceptibility toward necrotrophic Alternaria brassicicola. This is accompanied by constitutively elevated SA levels and SA-related marker gene expression, whereas JA-dependent markers are repressed. Conversely, UGT76B1 overexpression has the opposite effect. Thus, UGT76B1 attenuates SA-dependent plant defense in the absence of infection, promotes the JA response, and delays senescence. The ugt76b1 phenotypes were SA dependent, whereas UGT76B1 overexpression indicated that this gene possibly also has a direct effect on the JA pathway. Nontargeted metabolomic analysis of UGT76B1 knockout and overexpression lines using ultra-high-resolution mass spectrometry and activity assays with the recombinant enzyme led to the ab initio identification of isoleucic acid (2-hydroxy-3-methyl-pentanoic acid) as a substrate of UGT76B1. Exogenously applied isoleucic acid increased resistance against P. syringae infection. These findings indicate a novel link between amino acid-related molecules and plant defense that is mediated by small-molecule glucosylation.

Cytochromes p450

There are 244 cytochrome P450 genes (and 28 pseudogenes) in the Arabidopsis genome. P450s thus form one of the largest gene families in plants. Contrary to what was initially thought, this family diversification results in very limited functional redundancy and seems to mirror the complexity of plant metabolism. P450s sometimes share less than 20% identity and catalyze extremely diverse reactions leading to the precursors of structural macromolecules such as lignin, cutin, suberin and sporopollenin, or are involved in biosynthesis or catabolism of all hormone and signaling molecules, of pigments, odorants, flavors, antioxidants, allelochemicals and defense compounds, and in the metabolism of xenobiotics. The mechanisms of gene duplication and diversification are getting better understood and together with co-expression data provide leads to functional characterization.

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

BACKGROUND

The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data.

RESULTS

We present and justify a new technique for comparing pairs of networks--in our case these networks are based on the same set of nodes and there are two distinct types of weighted edges. The algorithm is based on the Generalized Singular Value Decomposition (GSVD), which may be regarded as an extension of Principle Components Analysis to the case of two data sets. We show how the GSVD can be interpreted as a technique for reordering the two networks in order to reveal clusters that are exclusive to only one. Here we apply this algorithm to a new set of metabolomic data from the prefrontal cortex (PFC) of a translational model relevant to schizophrenia, rats treated subchronically with the N-methyl-D-Aspartic acid (NMDA) receptor antagonist phencyclidine (PCP). This provides us with a means to quantify which predefined metabolic pathways (Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolite pathway database) were altered in the PFC of PCP-treated rats. Several significant changes were discovered, notably: 1) neuroactive ligands active at glutamate and GABA receptors are disrupted in the PFC of PCP-treated animals, 2) glutamate dysfunction in these animals was not limited to compromised glutamatergic neurotransmission but also involves the disruption of metabolic pathways linked to glutamate; and 3) a specific series of purine reactions Xanthine ← Hypoxyanthine ↔ Inosine ← IMP → adenylosuccinate is also disrupted in the PFC of PCP-treated animals.

CONCLUSIONS

Network reordering via the GSVD provides a means to discover statistically validated differences in clustering between a pair of networks. In practice this analytical approach, when applied to metabolomic data, allows us to quantify the alterations in metabolic pathways between two experimental groups. With this new computational technique we identified metabolic pathway alterations that are consistent with known results. Furthermore, we discovered disruption in a novel series of purine reactions that may contribute to the PFC dysfunction and cognitive deficits seen in schizophrenia.

Metabolomic approaches toward understanding nitrogen metabolism in plants

Plants can assimilate inorganic nitrogen (N) sources to organic N such as amino acids. N is the most important of the mineral nutrients required by plants and its metabolism is tightly coordinated with carbon (C) metabolism in the fundamental processes that permit plant growth. Increased understanding of N regulation may provide important insights for plant growth and improvement of quality of crops and vegetables because N as well as C metabolism are fundamental components of plant life. Metabolomics is a global biochemical approach useful to study N metabolism because metabolites not only reflect the ultimate phenotypes (traits), but can mediate transcript levels as well as protein levels directly and/or indirectly under different N conditions. This review outlines analytical and bioinformatic techniques particularly used to perform metabolomics for studying N metabolism in higher plants. Examples are used to illustrate the application of metabolomic techniques to the model plants Arabidopsis and rice, as well as other crops and vegetables.

Insulin sensitivity is reflected by characteristic metabolic fingerprints--a Fourier transform mass spectrometric non-targeted metabolomics approach

Background

A decline in body insulin sensitivity in apparently healthy individuals indicates a high risk to develop type 2 diabetes. Investigating the metabolic fingerprints of individuals with different whole body insulin sensitivity according to the formula of Matsuda, et al. (ISI_Matsuda) by a non-targeted metabolomics approach we aimed a) to figure out an unsuspicious and altered metabolic pattern, b) to estimate a threshold related to these changes based on the ISI, and c) to identify the metabolic pathways responsible for the discrimination of the two patterns.

Methodology and Principal Findings

By applying infusion ion cyclotron resonance Fourier transform mass spectrometry, we analyzed plasma of 46 non-diabetic subjects exhibiting high to low insulin sensitivities. The orthogonal partial least square model revealed a cluster of 28 individuals with alterations in their metabolic fingerprints associated with a decline in insulin sensitivity. This group could be separated from 18 subjects with an unsuspicious metabolite pattern. The orthogonal signal correction score scatter plot suggests a threshold of an ISI_Matsuda of 15 for the discrimination of these two groups. Of note, a potential subgroup represented by eight individuals (ISI_Matsuda value between 8.5 and 15) was identified in different models. This subgroup may indicate a metabolic transition state, since it is already located within the cluster of individuals with declined insulin sensitivity but the metabolic fingerprints still show some similarities with unaffected individuals (ISI >15). Moreover, the highest number of metabolite intensity differences between unsuspicious and altered metabolic fingerprints was detected in lipid metabolic pathways (arachidonic acid metabolism, metabolism of essential fatty acids and biosynthesis of unsaturated fatty acids), steroid hormone biosyntheses and bile acid metabolism, based on data evaluation using the metabolic annotation interface MassTRIX.

Conclusions

Our results suggest that altered metabolite patterns that reflect changes in insulin sensitivity respectively the ISI_Matsuda are dominated by lipid-related pathways. Furthermore, a metabolic transition state reflected by heterogeneous metabolite fingerprints may precede severe alterations of metabolism. Our findings offer future prospects for novel insights in the pathogenesis of the pre-diabetic phase.